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Learning-To-Rank

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).

Papers

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Showing 561–570 of 753 papers

Title	Date	Tasks	Status
Semantic Relatedness of Wikipedia Concepts -- Benchmark Data and a Working Solution	May 1, 2018	Entity LinkingLearning-To-Rank	—Unverified
Semi-Automatic Construction of Word-Formation Networks (for Polish and Spanish)	May 1, 2018	Learning-To-RankSequential Pattern Mining	—Unverified
A General Framework for Counterfactual Learning-to-Rank	Apr 30, 2018	counterfactualCounterfactual Inference	—Unverified
Unbiased Learning to Rank with Unbiased Propensity Estimation	Apr 16, 2018	Learning-To-Rankparameter estimation	CodeCode Available
Learning a Deep Listwise Context Model for Ranking Refinement	Apr 16, 2018	Information RetrievalLearning-To-Rank	CodeCode Available
Application of the Ranking Relative Principal Component Attributes Network Model (REL-PCANet) for the Inclusive Development Index Estimation	Apr 16, 2018	Learning-To-Rank	—Unverified
Local Descriptors Optimized for Average Precision	Apr 15, 2018	Learning-To-RankRetrieval	—Unverified
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns	Mar 20, 2018	Incremental LearningLearning-To-Rank	CodeCode Available
Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets	Mar 14, 2018	feature selectionInformation Retrieval	—Unverified
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank	Mar 8, 2018	Crowd CountingImage Retrieval	CodeCode Available

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